• Journal of Korean Society of Coastal and Ocean Engineers
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• v.30 no.6
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• pp.326-336
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• 2018

Continuous measurement of nearshore waves around Korea over long period is very demanding to setup plans for prevention of disasters of port and coastal structures. In this respect, a new web-based system, termed as WINK, was established, which collects nearshore wave data from Korea Meteorological Agency (KMA), Korea Hydrographic and Oceanographic Agency (KHOA), and Ministry of Oceans and Fisheries (MOF) and provide them after quality control of the data. This paper describes technical aspects regarding collection and selection of the wave observation data, construction of wave hindcasting data, the methodology of quality control for the selected wave data, and overall process of building the web-based data providing system.

• The Korean Journal of Applied Statistics
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• v.32 no.1
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• pp.69-81
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• 2019

We consider bivariate long range dependent (LRD) time series forecasting using a deep learning method. A long short-term memory (LSTM) network well-suited to time series data is applied to forecast bivariate time series; in addition, we compare the forecasting performance with bivariate fractional autoregressive integrated moving average (FARIMA) models. Out-of-sample forecasting errors are compared with various performance measures for functional MRI (fMRI) data and daily realized volatility data. The results show a subtle difference in the predicted values of the FIVARMA model and VARFIMA model. LSTM is computationally demanding due to hyper-parameter selection, but is more stable and the forecasting performance is competitively good to that of parametric long range dependent time series models.

• Journal of Power Electronics
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• v.19 no.2
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• pp.403-412
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• 2019

Aiming at the problems of large dv/dt and di/dt in traditional single-ended converters and high electromagnetic interference (EMI) noise levels, a single-ended isolated converter using the secondary resonance technique is proposed in this paper. In the proposed converter, the voltage stress of the main power switch can be reduced and the voltage across the output diode is clamped to the output voltage when compared to the conventional flyback converter. In addition, the peak current stress through the main power switch can be decreased and zero current switching (ZCS) of the output diode can be achieved through the resonance technique. Moreover, the EMI noise coupling path and an equivalent model of the proposed converter topology are presented through the operational principle of the proposed converter. Analysis results indicate that the common mode (CM) EMI noise and the differential mode (DM) EMI noise of such a converter are deduced since the frequency spectra of the equivalent controlled voltage sources and controlled current source are decreased when compared with the traditional flyback converter. Furthermore, appropriate parameter selection of the resonant circuit network can increase the equivalent impedance in the EMI coupling path in the low frequency range, which further reduces the common mode interference. Finally, a simulation model and a 60W experimental prototype of the proposed converter are built and tested. Experimental results verify the theoretical analysis.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.12 no.1
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• pp.58-71
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• 2019

This paper proposes architecture of a cognitive radio engine platform and the allowable frequency channel reasoning method that enables acquisition of the allowable channels for the military tactical network environment. The current military tactical wireless communication system is increasing need to secure a supplementary radio frequency to ensure that multiple wireless networks for different military wireless devices coexist, so that tactical wireless communication between the same or different systems can be operated effectively. This paper presents the allowable frequency channel reasoning method based on cognitive radio engine for realizing DSA(Dynamic Spectrum Access) as an optimal available frequency channel. To this end, a case-based allowable frequency channel reasoning method for cognitive radio devices is proposed through modeling of primary user's traffic status and calculation of channel occupancy probability. Also through the simulation of the performance analysis, changing rate of collision probability between the primary users' occupancy channel and the available channel acquisition information that can be used by the cognitive radio device was analysed.

• The Journal of the Convergence on Culture Technology
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• v.5 no.1
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• pp.429-434
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• 2019

There are several high technologies applied to the driving cars such as self-driving car and connected car for safe and convenient driving. VANET provides useful information such as route selection and gas price by communicating nearby cars and RSUs. VANET prefers CCN rather than traditional TCP/IP stack because CCN offers inherent multicast communication for sharing traffic information as well as traditional unicast. When all participating node rebroadcasts the Interest packets in a Vehicular CCN, the network may suffer from Broadcast Storm Problem. In order to mitigate the effect of the problem and to improve the Data packet transmission, not all but some selected nodes have to rebroadcast the packet. This paper simulates car movements using SUMO and evaluates data transmission performance using ns-3. According to the simulation results, when some selected nodes rebroadcast the Interest packets, the transmission performance improves 10% to 25% depending on the number of requesting nodes.

• Asian-Australasian Journal of Animal Sciences
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• v.32 no.8
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• pp.1084-1094
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• 2019

Objective: The aim of this study was to select the candidate genes affecting meat quality and preliminarily explore the related molecular mechanisms in the Mashen pig. Methods: The present study explored genetic factors affecting meat quality in the Mashen pig using RNA sequencing (RNA-Seq). We sequenced the transcriptomes of 180-day-old Mashen and Large White pigs using longissimus dorsi to select differentially expressed genes (DEGs). Results: The results indicated that a total of 425 genes were differentially expressed between Mashen and Large White pigs. A gene ontology enrichment analysis revealed that DEGs were mainly enriched for biological processes associated with metabolism and muscle development, while a Kyoto encyclopedia of genes and genomes analysis showed that DEGs mainly participated in signaling pathways associated with amino acid metabolism, fatty acid metabolism, and skeletal muscle differentiation. A MCODE analysis of the protein-protein interaction network indicated that the four identified subsets of genes were mainly associated with translational initiation, skeletal muscle differentiation, amino acid metabolism, and oxidative phosphorylation pathways. Conclusion: Based on the analysis results, we selected glutamic-oxaloacetic transaminase 1, malate dehydrogenase 1, pyruvate dehydrogenase 1, pyruvate dehydrogenase kinase 4, and activator protein-1 as candidate genes affecting meat quality in pigs. A discussion of the related molecular mechanisms is provided to offer a theoretical basis for future studies on the improvement of meat quality in pigs.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.18 no.2
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• pp.97-105
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• 2008

The various mutations of the malicious codes are fast generated on the network. Also the behaviors of them become intelligent and the damage becomes larger step by step. In this paper, we suggest the method to select the useful measures for the detection of the codes. The method has the advantage of shortening the detection time by using header data without payloads and uses connection data that are composed of TCP/IP packets, and much information of each connection makes use of the measures. A naive estimator is applied to the probability distribution that are calculated by the histogram estimator to select the specific measures among 80 measures for the useful detection. The useful measures are then selected by using relative entropy. This method solves the problem that is to misclassify the measure values. We present the usefulness of the proposed method through the result of the detection experiment using the detection patterns based on the selected measures.

• Journal of Information Science Theory and Practice
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• v.8 no.4
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• pp.40-54
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• 2020

This proposed work aims to understand the Korean Academic Circle (KAC)'s research trend on the "One Belt One Road" (OBOR) by employing a quantitative analysis of the recent research articles published by the KAC. To do so, this proposed research has used the well-known network analysis software, Ucinet 6, by which the papers on related topics are collected and filtered from Korea Citation Index. To perform the analytical selection, the proposed work has chosen 'keywords' as the core research object and performed analysis from transverse to longitudinal aspects, and from holistic to individual aspects, respectively; and from this, the KAC's research trend on OBOR is derived. The present work has established that the KAC's attention is continuously increasing on OBOR and has sustainability. Centered on the OBOR, Korean researchers have spread their studies in various dimensions ranging from the issues like China's political economy to Sino-Korea economic and trade exchanges, and so on. The KAC has even combined OBOR with Korea's international development initiatives, which can help Korea benefit from active and sustainable cooperation with China. Moreover, the proposed work has found that Korean researchers have also actively expressed their growing attention, highlighted Korea's interest, and showed concern about China hegemony and Sinocentrism in their recent documented research works.

• Journal of Korea Foundry Society
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• v.42 no.4
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• pp.209-217
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• 2022

In the present study, in order to prevent the misrun defects that occur during traditional brass casting, a method for selecting the proper casting process conditions is proposed. A learning model was developed and demonstrated to be able to learn the presence or absence of defects according to the casting process conditions and to predict the occurrence of defects depending on the certain process given. Appropriate process conditions were determined by applying the proposed method, and the determined conditions were verified through a comparison of different simulation results with additional conditions. With this method, it is possible to determine the casting process conditions that will prevent defects in the desired sand model. This technology is expected to contribute to realization of smart traditional brass farming workshops.

• The Journal of Information Systems
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• v.31 no.3
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• pp.197-220
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• 2022

Purpose The purpose of this study is to identifies the demand pattern categorization of repair parts of Automotive After-sales Service(A/S) and proposes a demand prediction model for Auto repair parts using Long Short-Term Memory (LSTM) of artificial neural networks (ANN). The optimal parts inventory quantity prediction model is implemented by applying daily, weekly, and monthly the parts demand data to the LSTM model for the Lumpy demand which is irregularly in a specific period among repair parts of the Automotive A/S service. Design/methodology/approach This study classified the four demand pattern categorization with 2 years demand time-series data of repair parts according to the Average demand interval(ADI) and coefficient of variation (CV²) of demand size. Of the 16,295 parts in the A/S service shop studied, 96.5% had a Lumpy demand pattern that large quantities occurred at a specific period. lumpy demand pattern's repair parts in the last three years is predicted by applying them to the LSTM for daily, weekly, and monthly time-series data. as the model prediction performance evaluation index, MAPE, RMSE, and RMSLE that can measure the error between the predicted value and the actual value were used. Findings As a result of this study, Daily time-series data were excellently predicted as indicators with the lowest MAPE, RMSE, and RMSLE values, followed by Weekly and Monthly time-series data. This is due to the decrease in training data for Weekly and Monthly. even if the demand period is extended to get the training data, the prediction performance is still low due to the discontinuation of current vehicle models and the use of alternative parts that they are contributed to no more demand. Therefore, sufficient training data is important, but the selection of the prediction demand period is also a critical factor.